In the past few decades, the petroleum industry has invested heavily in the development of marine seismic survey techniques that yield knowledge of subterranean formations beneath a body of water in order to find and extract valuable mineral resources, such as oil. High-resolution seismic images of a subterranean formation are essential for quantitative seismic interpretation and improved reservoir monitoring. For a typical marine seismic survey, an exploration-seismology vessel tows a seismic source and one or more streamers that form a seismic data acquisition surface below the surface of the water and over a subterranean formation to be surveyed for mineral deposits. The vessel contains seismic acquisition equipment, such as navigation control, seismic source control, seismic receiver control, and recording equipment. The seismic source control causes the seismic source, which is typically an array of source elements, such as air guns, to produce acoustic impulses at selected times. Each impulse is a sound wave that travels down through the water and into the subterranean formation. At each interface between different types of rock, a portion of the sound wave is refracted and another portion is reflected back toward the body of water to propagate toward the surface. The streamers towed behind the vessel are elongated cable-like structures. Each streamer includes a number of seismic receivers or sensors that detect pressure and/or velocity wavefields associated with the sound waves reflected back into the water from the subterranean formation.
Because marine surveys are traditionally conducted near the free surface of an open body of water, such as an ocean, sea, or lake, the survey data may be impacted by conditions at the water surface. For example, swell noise can be a significant problem in offshore surveys. Swell noise results from swells, which are a series of surface waves generated by rough weather conditions. Because swells have dispersed from their source, swells typically have a longer wavelength than local wind generated waves and occur as large-amplitude, low-frequency noise (e.g., between about 0-20 Hz) that can be observed in geophysical images (e.g., seismic images). FIG. 1 shows a plot of a gather generated by hydrophones in a marine survey. The swell noise can be identified in FIG. 1 as vertical stripes. Swell noise adversely effects seismic data quality and may be severe enough to suspend a survey.
Typical industry solutions for swell noise attenuation use overlapping local time-space (“t-x”) windows that cover the entire input seismic gather section. The windows are filtered independently and then merged to construct the output seismic gather section. Each window is mapped to the frequency-space (“f-x”) domain and processed in two stages. The first stage is the detection of swell noise locations in the f-x spectrum and the second stage is removal of noise by interpolation using a projection/prediction error filter (“PEF”) (see e.g., “Signal-preserving random noise attenuation by the f-x projection,” by R. Soubaras, SEG Expanded Abstracts 13, pp. 1576-1579, 1994). Swell noise detection is often the computational bottleneck and any improvement in the detection of swell noise improves the performance of noise detection and filtering. A potential problem with the current industry's solutions to remove swell noise is that all of the local data windows work independently. There is no coordination between data windows in terms of noise detection. In other words, when a given trace in a data window is considered noisy, there is no guarantee that the same trace that falls in an adjacent window will also be considered noisy, which contradicts the fact that swell noise typically contaminates an entire trace from time zero to the end of the record. Such inconsistency in the swell noise detection is the main cause of signal distortion, where large signal amplitude (e.g. direct arrival) is often mistakenly considered as swell noise because it has locally strong amplitude.
While detection of swell noise may be improved by increasing the dimensions of the data window, such as increasing the number of traces and the number of samples in each trace (i.e., more statistics and larger spectral amplitudes), the filtering performance using f-x prediction or any other models is not necessarily improved. Because both the detection step and the filtering step are done in the same t-x data window, grouping creates a trade-off for the user in terms of parameter setting. Those working in the petroleum industry seek computational systems and methods that accurately detect swell noise in geophysical data so the swell noise can be removed.